Accurate and prompt property prediction plays an essential role in discovering and designing novel materials.
First principle (viz., ab initio) methods [1]} are commonly used to calculate the properties of candidate materials.
However, ab initio methods based on either Density-Functional Theory (DFT) or wavefunction theories are relatively time-consuming.
An alternative method is to predict these properties using machine learning (ML) technologies. By modeling the material structure and atomic interactions, ML algorithms are much faster than DFT and are expected to provide similar accuracy [2]} .
